Triple
T1177401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geneva tram network |
E25058
|
entity |
| Predicate | hasDepot |
P2413
|
FINISHED |
| Object | Meyrin depot |
E47537
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Meyrin depot | Statement: [Geneva tram network, hasDepot, Meyrin depot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meyrin depot Context triple: [Geneva tram network, hasDepot, Meyrin depot]
-
A.
Fürth depot
Fürth depot is a maintenance and storage facility serving the Nuremberg U-Bahn rapid transit system in the Fürth area of Germany.
-
B.
Meyrin
chosen
Meyrin is a municipality in the canton of Geneva, Switzerland, best known for hosting major CERN facilities including the Super Proton Synchrotron.
-
C.
Carnide depot
Carnide depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
-
D.
Pontinha depot
Pontinha depot is a maintenance and storage facility serving the Lisbon Metro system in Lisbon, Portugal.
-
E.
Südkreuz station
Südkreuz station is a major Berlin transport hub serving regional, long-distance, and S-Bahn trains in the southern part of the city.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd0ebbd08190a441d16a5b65a15e |
completed | March 1, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac6f1cab308190bdb5ae1e01d83b61 |
completed | March 7, 2026, 6:31 p.m. |
Created at: March 1, 2026, 7:45 p.m.